Musical tracks (or songs) are typically labelled by listeners in a variety of ways, including genre (pop, rock, rap, country, heavy metal, hip-hop, grunge, etc.) occasion (Christmas, Halloween, wedding, driving, etc.) mood (romantic, depressed, etc.) and others. Tracks stored in computerized play systems are frequently stored with one or more such labels or tags associated with them. Each track may have numerous tags associated with it. For example, a single song may be tagged as “pop,” “Christmas,” and “romantic.” Such tags, however, are generally added manually and are frequently inconsistent between songs, as tags may rely on the music producers to supply tags. Each producer may have inconsistent views on what constitutes a given tag or even over-tag songs in an effort to encourage more play.
A variety of methods have been used to systematically tag tracks in a consistent way. While advancements have been made in such automated tagging, technical challenges still remain. Previous efforts have required significant human intervention and tuning to, for example, label a set of tracks with a single genre, each.
Listeners also frequently seek to create playlists of music with similar songs. Typical methods for playlist creation frequently relate to using pre-generated song tags to choose songs with similar sets of tags. One technical problem with creating playlists of music with similar songs involves generating playlists based on a seed song where the members of the playlist are lyrically similar or a combination of acoustically and lyrically similar to the seed song.
A common problem for music is to determine whether a song should be considered explicit or not. Some parents may not wish younger listeners to hear music with certain words or ideas contained within. One challenge for determining whether a given song is explicit is that some may consider a song explicit even if no individual word would be considered explicit. For example, concepts or sex or violence may be expressed more clearly than some parents wish without any individually objectionable words. Currently, music providers rely on the determination of the music producers to label certain tracks as explicit. There exists a need for a flexible, automatic method for training a system to classify music as explicit or not, based on a sample set.
US 2008/0147215 describes generating music recommendations based on input of theme, mood, and selected features. The input is compared to a library of pre-tagged and processed musical tracks to determine similar songs for recommendation.
US 2014/0214848 describes a system for generating a playlist based upon the mood of the user. The system determines the mood of the user and searches a library of music for comparable tracks by comparing the mood to pre-generated emotion tags associated with the music.
Dawen Liang, Haijie Gu, and Brendan O'Connor, Music Classification with the Million Song Dataset 15-826 Final Report, Carnegie Mellon University (Dec. 3, 2011) (available at http://www.ee.columbia.edu/˜dliang/files/FINAL.pdf) describes a method for predicting a single genre for a set of songs by training a classifier with acoustical and lyrical information based, in part, on human-constructed emotional valence features for lyrics. The system compared the acoustics and lyrics of songs to determine to which genre a given song most closely matched.
Ruth Dhanaraj and Beth Logan, Automatic Prediction of Hit Songs, HP Laboratories Cambridge (Aug. 17, 2005) (available at http://www.hpl.hp.com/techreports/2005/HPL-2005-149.pdf) describes generating classifiers using lyrics and acoustic-based vectors to determine which songs were more likely to become hits.